Vertex AI Archives - Liwaiwai

Built With BigQuery And Google AI: How Glean Enhances Enterprise Search Quality And Relevance For Teams

Context AboutGlean Glean searches across all your company’s apps to help you find exactly what you need and discover the information you need to do your best work. It delivers powerful, unified enterprise search across all workplace applications, websites, and data sources used within an enterprise. Search results respect the existing permissions from your company’s systems, so users only see what they already have permission to see. Glean’s enterprise search also takes into account your role, projects, collaborators, and the language and acronyms specific to your company to deliver highly personalized results that provide you with information most pertinent to…

How To Use Advance Feature Engineering To Preprocess Data In BigQuery ML

Preprocessing and transforming raw data into features is a critical but time consuming step in the ML process. This is especially true when a data scientist or data engineer has to move data across different platforms to do MLOps. In this blogpost, we describe how we streamline this process by adding two feature engineering capabilities in BigQuery ML. Our previous blog outlines the data to AI journey with BigQuery ML, highlighting two powerful features that simplify MLOps – data preprocessing functions for feature engineering and the ability to export BigQuery ML TRANSFORM statement as part of the model artifact. In…

Data

Reading And Storing Data For Custom Model Training On Vertex AI

Before you can train ML models in the cloud, you need to get your data to the cloud. But when it comes to storing data on Google Cloud there are a lot of different options. Not to mention the different ways you can read in data when designing input pipelines for custom models. Should you use the Cloud Storage API? Copy data directly to the machine where your training job is running? Use the data I/O library of your preferred ML framework? To make things a little easier for you, we’ve outlined some recommendations for reading data in your custom…

Vertex AI Foundations For Secure And Compliant ML/AI Deployment

An increasing number of Enterprise customers are adopting ML/AI as their core transformational pillars, in order to differentiate, increase revenue, reduce costs and maximize efficiency. For many customers ML/AI adoption can be a challenging endeavor not only because of the broad spectrum of applications ML/AI can support, deciding on which one to prioritize can be a challenge, but because moving these solutions into production require a series of security, access and data assessments and features that some ML/AI platforms might not have. This blog post focuses on how to set up your Cloud foundations to cater specifically to the Vertex…

RAD Lab AlphaFold Module For researchers

Understanding protein geometries is essential to understanding a protein’s function, and thus essential to a range of disruptive research projects, from creating more sustainable materials to developing more effective treatments to diseases. For this reason, the “protein-folding problem” ranked for decades among biological science’s grand challenges—until Google’s DeepMind developed AlphaFold.Widely hailed as a breakthrough in biological research and a leap in the development of vaccines and synthetic materials, AlphaFold is an AI model developed by DeepMind for predicting the 3D structure of a protein based on its 1D amino acid sequence. DeepMind open-sourced the AlphaFold algorithm, code, and methodology with the scientific community in…

SAVI Transforms Global Surgical Instrument Tracking With Google Cloud

Powered by Vertex AI (Google Cloud’s platform for accelerating development and deployment of machine learning models into production), SAVI (Semi Automated Vision Inspection)1 is transforming surgical instrument identification and cataloging, leading to fewer canceled surgeries and easing pressure on surgery waitlists. Max Kelsen, an analytics and software agency that specializes in machine learning, has worked closely with Google Cloud and Johnson & Johnson MedTech to create a system that can manage tens of thousands of individual devices, their characteristics, and how they apply to each set or tray used by a surgeon. SAVI does this while delivering a one in…

How To Optimize Training Performance With The TensorFlow Profiler On Vertex AI

Training ML models can be computationally expensive. If you’re training models on large datasets, you might be used to model training taking hours, or days, or even weeks. But it’s not just a large volume of data that can increase training time. Nonoptimal implementations such as an inefficient input pipeline or low GPU usage can dramatically increase your training time. Making sure your programs are running efficiently and without bottlenecks is key to faster training. And faster training makes for faster iteration to reach your modeling goals. That’s why we’re excited to introduce the TensorFlow Profiler on Vertex AI, and…

Improving Model Quality At Scale With Vertex AI Model Evaluation

Typically, data scientists retrain models at regular intervals to keep them fresh and relevant. This practice may turn out to be costly if the model is trained too often or inefficient if the model training isn’t frequent enough to serve the business. Ideally, data scientists prefer to continuously evaluate the models and intentionally retrain models when the model performance starts to degrade. At scale, continuous model evaluation would require a standard and efficient evaluation process and system.In fact, after training a model, data scientists and ML engineers use an offline dataset of historical examples from the production environment to evaluate…

Movie Score Prediction With BigQuery, Vertex AI And MongoDB Atlas

Hey there! It’s been a minute since we last wrote about Google Cloud and MongoDB Atlas together. We had an idea for this new genre of experiment that involves BigQuery, BQML, Vertex AI, Cloud Functions, MongoDB Atlas, and Cloud Run and we thought of putting it together in this blog. You will get to learn how we brought these services together in delivering a full stack application and other independent functions and services the application uses. Have you read our last blog about Serverless MEAN stack applications with Cloud Run and MongoDB Atlas? If not, this would be a good…

Boost Medical Discoveries With AlphaFold On Vertex AI

There’s a lot we can learn from combining technology with science to help support the development of amazing discoveries. By using an AI system to predict protein shapes, we have the potential to accelerate research in every field of biology. Inside every cell in your body, billions of tiny molecular machines are hard at work. They are what allow your eyes to detect light, your neurons to fire, and the ‘instructions’ in your DNA to be read. These intricate machines are known as proteins. The protein folding puzzle Protein folding is something that occurs naturally so that proteins become biologically…

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